[Music]
when it was a boy I wanted to maximize
my impact on the world and I was smart
enough to realize that I am NOT very
smart and that I have to build a machine
that learns to become much smarter than
myself such that it can solve all the
problems that I cannot solve myself and
I can retire and my first publication on
that dates back 30 years 1987 my diploma
thesis where I already try to solve the
grand problem of AI not only build a
machine that learns a little bit here
and learns a little bit there but also
learns to improve the learning algorithm
itself and the way it learns the way it
learns and so on recursively without any
limits except the limits of logics and
physics and I'm still working on the
same old thing and I'm still pretty much
saying the same thing except that now
more people are listening because the
learning algorithms that we have
developed on the way to this goal they
are now on three thousand million
smartphones and all of you have them in
your pockets what you see here are the
five most valuable companies of the
Western world Apple Google Facebook
Microsoft and Amazon and all of them are
emphasizing that AI artificial
intelligence is central to what they are
doing and all of them are using heavily
the deep learning methods that my team
has developed since the early nineties
and yone
in Munich and in Switzerland especially
something which is called so long short
term memory has anybody in this room
ever heard of the long short-term memory
or the LST M hands up anybody ever heard
of that okay
has anybody never heard of the STM I see
we have a third group in this room
who didn't understand the question
the lsdm is a little bit like your brain
it's an artificial neural network which
also has neurons and in your brain
you've got about 100 billion neurons and
each of them is connected to roughly
10,000 other neurons on average which
means that you have got a million
billion connections and each of these
connections has a strength which says
how much does this neuron over here
influence that neuron over there at the
next time step and in the beginning all
these connections are random and the
system knows nothing within through a
smart learning algorithm it learns from
lots of examples to translate the
incoming data such as video through the
cameras or audio through the microphones
or pain signals through the pain sensors
it learns to translate that into output
actions because some of these neurons
are output neurons that control speech
muscles and finger muscles and only
through experience it can learn to solve
all kinds of interesting problems such
as driving a car or do the speech
recognition on your smartphone because
whenever you take out your smartphone an
Android phone for example and you speak
to it and you say ok Google show me is
the shortest way to Milano then it
understands your speech because there is
an lsdm in there which has learned to
understand speech every 10 milliseconds
100 times a second new inputs are coming
from the microphone and then translates
it after thinking into letters which is
then question to the search engine and
it has long to do that by listening to
lots of speech from women from me
all kinds of people and that's how since
2015 Google speech recognition is now
much better than it used to be the basic
lsdm cell looks like that I don't have
the time to explain that but at least I
can list the names of the brilliant
students in my lab who made that
possible and what are the big companies
doing with that
well speech recognition is only one
example if you are on Facebook is
anybody on Facebook okay I use sometimes
clicking at the translate button because
somebody sent you something in a foreign
language and then you can translate it
is anybody doing that yeah whatever you
do that you are waking up again a long
short term memory and lsdm which has
learned to translate text in one
language into translated text and
Facebook is doing that four billion
times a day so every 50 every second
50,000 sentences are being translated by
an LST am working for Facebook and
another 50,000 in the second and another
50,000 and to see how much this thing is
now permitting the modern world just
note that almost 30 percent of the
awesome computational power for
interference and all these Google Data
Centers all these data centers of Google
are all over the world is used for LST
on almost 30 percent if you have an
Amazon echo you can ask a questions and
it answers you and the voice that you
hear it's not a recording it's an LS TM
network which has learned from training
examples to sound like a female voice if
you have an iPhone and you're using the
quick type it's trying to predict what
you want to do next given all the
previous context of what you did so far
again that's an LS DM which has
to do that so it's on a billion iPhones
you are a large audience by my standards
but when we started this work decades
ago in the early 90s only few people who
were interested in that
because computers were so slow and you
couldn't do so much with it and I
remember I gave a talk at a conference
and there was just one single person in
the audience a young lady I said young
lady it's very embarrassing but
apparently today I'm going to give this
talk just to you and she said okay but
please hurry I am the next speaker
since then we have greatly profited from
the fact that every five years computers
again in ten times cheaper which is an
old trend that has held since 1941 at
least since this man Conrad Susan built
the first working program control
computer in Berlin and he could could do
roughly one operation per second one and
then ten years later for the same prize
one could do 100 operations 30 years
later 1 million operations were the same
price and today after 75 years we can do
a million billion times as much for the
same price and the trend is not about to
stop because the physical limits are
much further out there rather soon and
not so many years or decades we will for
the first time have little computational
devices that can compute as much as a
human brain and this a trend doesn't
break 50 years later there will be a
little computational device for the same
price that can compute as much as all 10
billion human brains taken together and
there will not only be one of those
devices but many many many everything is
going to change already in 2011
computers were fast enough such that our
deep learning methods for the first time
could achieve a superhuman
pattern-recognition result and was the
first superhuman result and the history
of computer vision and back then
computers were 20 times more expensive
than today so today for the same price
we can do 20 times as much and just a
few five years ago five years ago when
computers were 10 times more expensive
than today
we already could win for the first time
medical imaging competitions what you
see behind me is a slice through the
female breast and the tissue that you
see there has all kinds of cells and
normally you need a trained doctor a
trained the solid
who is able to detect the dangerous
cancer cells or pre-cancer cells now our
stupid network knows nothing about
cancer knows nothing about vision it
knows nothing in the beginning but we
can train it to imitate the human
teacher the doctor and it became as good
or better than the best competitors and
very soon all of medical diagnosis is
going to be superhuman and it's going to
be mandatory because it's going to be so
much better than the doctors after this
all kinds of medical imaging startups
were founded focusing just on this
because it's so important we can also
use lsdm to train robots one important
thing I want to say is that we not only
have systems that slavishly
imitate what humans show them no we also
have a eyes that set themselves their
own goals and like little babies invent
their own experiment to explore the
world and to figure out what you can do
in the world without a teacher and
becoming more and more general problem
solvers in the process by learning new
skills on top of old skills and this is
going to scale we call that artificial
curiosity or a recent password is power
plain learning to become a more and more
general problems over by learning to
invent like a scientist one new
interesting goal after Nathan and and
it's going to scale and I think in not
so many years from now for the first
time we are going to have an animal like
AI you don't have that yet on the level
of a little crowd which already can
learn to use two worlds for example
little monkey and once we have that it
may take just a few decades to do the
final step towards human level
intelligence because technological
evolution is about a million times
a million times faster than biological
evolution and biological evolution
needed 3.5 billion years to evolve a
monkey a monkey from scratch but then
just a few tens of millions of years
afterwards to evolve human level
intelligence we have a company which is
called Mason's like birth in English
Mason's but spelled in a different way
which is trying to make this a reality
and build the first true general and
purpose AI at the moment almost all
research in AI is very human centric and
it's all about making human lives longer
and healthier and easier and making
humans more addicted to their
smartphones but in the long run a eyes
are going to especially the smart ones
are going to set themselves their own
goals and I have no doubt in my mind
that they are going to become much
smarter than we are
and what are they going to do of course
they are going to realize what we have
realized a long time ago namely that
most of the resources in the solar
system or in general are not in our
little biosphere they are out there in
space and so of course they are going to
emigrate and of course they are going to
use trillions of self-replicating robot
factories to expand in form of growing
AI bubble which within a few hundred
thousand years is going to cover the
entire galaxy by senders and receivers
such that a eyes can travel the way they
are already traveling in my lab by radio
from sender to receiver Wireless so what
we are witnessing now is much more than
just another Industrial Revolution this
is something that transcends humankind
and even life itself the last time
something so important has happened was
maybe 3.5 billion years ago when life
was invented a new type of life is going
to emerge from our little planet and
it's going to colonize and transform the
entire universe the universe is still
young it's only 13.8 billion years old
it's going to become much older than
that many times more many times older
than that so there's plenty of time to
reach all of it or all of the visible
parts totally within the limits of light
speed and physics a new type of life is
going to make the universe intelligent
now of course we are not going to remain
the crown of creation of course not but
there is still beauty in seeing yourself
as part of a grander process that leads
the cosmos from low complexity towards
higher complexity it's a privilege to
live at a time where we can witness the
beginnings of that and where we can
contribute something to that thank you
for your patience